Speaker
Description
Emerging variants are a key driver of long-term dynamics in pathogens such as SARS-CoV-2 and influenza, yet most transmission models do not consider the dynamic nature of the mutation process.
We introduce a unified framework that couples multi-variant transmission with an explicit mutation process. Transmission is described by a generic compartmental model accounting for multiple variants with partial cross-immunity and seasonality. Variant emergence is represented by an inhomogeneous Poisson process whose intensity is proportional to the population-level infectious fraction. The resulting system is a stochastic hybrid model of variable dimension, as each mutation event extends the state space.
Using deterministic approximations and stochastic simulations, we characterize how dynamically arising variants shift the system away from the classical endemic equilibrium and generate sustained multi-wave dynamics. We identify parameter regimes in which an intermediate mean waiting time between variant emergences maximizes the number of infections. Finally, we illustrate how the joint treatment of transmission and mutation affects the evaluation of vaccination strategies and discuss implications for the design of robust control policies against rapidly evolving pathogens.
